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Vol. 160, Issue 12, December 2013, pp. 437-444




An Improved Kalman Filtering Algorithm for Maneuvering Target Tracking Based on ANFIS
Zengqiang MA, Yacong Zheng, Jianhua LIANG, Dongxing LI

School of Electrical and Electronics Engineering, Shijiazhuang Tiedao University, 050043, China
Tel.: 86-0311-87939226
E-mail: mzqlunwen@126.com


Received: 26 September 2013   /Accepted: 22 November 2013   /Published: 30 December 2013

Digital Sensors and Sensor Sysstems


Abstract: Kalman filtering is widely used in maneuvering target tracking. However, conventional Kalman filtering always fails to track the maneuvering target when there is a sudden change in the target motion state. In this paper, an improved Kalman filtering algorithm which based on the adaptive neural fuzzy inference system (ANFIS) is proposed. The improved algorithm can update the measurement noise covariance in real-time by ANFIS through observing the covariance matrix of Kalman residual. In this way, the tracking error can be reduced. Then, the comparison and analysis of the experiment results between the original Kalman filtering algorithm and the improved one have been carried out. The experiment results show that the tracking error is obviously reduced and the accuracy is significantly boosted after the original Kalman filtering algorithm was substituted by the improved one. To explore the influence of ANFIS parameters on the tracking error, the setting rules of them were analyzed at the end of this paper.


Keywords: Kalman filter, Maneuvering target tracking, Adaptive Neuro-Fuzzy Inference System, Covariance matrix, Parameter setting rules.


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